Fetching the paper…
Reading the bibliography…
We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time -- in other words, how can we train a model to be fair by race when we don't have data about race? We adopt a fairness pipeline perspective, in which an "upstream" learner that does have access to the sensitive features will learn a proxy model for these features from the other attributes.
Decision theoretic generalizations of the pac model for neural net and other learning applications
David Haussler · 1992
Earlier work this paper cites.
Game theory, on-line prediction and boosting
Yoav Freund and Robert E. Schapire · 1996
Earlier work this paper cites.
Efficient algorithms for online decision problems
Adam Kalai and Santosh Vempala · 2003
Earlier work this paper cites.
Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
Earlier work this paper cites.
Using the census bureau’s surname list to improve estimates of race/ethnicity and associated disparities
Marc N Elliott, Peter A Morrison, Allen Fremont, Daniel F McCaffrey, Philip Pantoja, and Nicole Lurie · 2009
Earlier work this paper cites.
Hubert Haoyang Duan · 2011
Earlier work this paper cites.
Amanda Bower, Sarah N Kitchen, Laura Niss, Martin J Strauss, Alexander Vargas, and Suresh Venkatasubramanian · 2017
Earlier work this paper cites.
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
Michael Veale and Reuben Binns · 2017
Earlier work this paper cites.
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Earlier work this paper cites.
Multicalibration: Calibration for the (computationally-identifiable) masses
Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Cited alongside, same era.
Blind justice: Fairness with encrypted sensitive attributes
Niki Kilbertus, Adrià Gascón, Matt Kusner, Michael Veale, Krishna Gummadi, and Adrian Weller · 2018
Cited alongside, same era.
Using first name information to improve race and ethnicity classification
Ioan Voicu · 2018
Cited alongside, same era.
Assessing fair lending risks using race/ethnicity proxies
Yan Zhang · 2018
Cited alongside, same era.
Fairness under unawareness: Assessing disparity when protected class is unobserved
Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, and Madeleine Udell · 2019
Equalized odds postprocessing under imperfect group information
Pranjal Awasthi, Matthäus Kleindessner, and Jamie Morgenstern · 2020
Later among the works it cites.
Individual fairness in pipelines
Cynthia Dwork, Christina Ilvento, and Meena Jagadeesan · 2020
Later among the works it cites.
Fairness without demographics through adversarially reweighted learning
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H Chi · 2020
Later among the works it cites.
Robust optimization for fairness with noisy protected groups
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, and Michael I Jordan · 2020
Later among the works it cites.
Evaluating fairness of machine learning models under uncertain and incomplete information
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, and Xuezhi Wang · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Differentially private fair learning
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman · 2019
Cited alongside, same era.
Downstream effects of affirmative action
Sampath Kannan, Aaron Roth, and Juba Ziani · 2019
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
Cited alongside, same era.
Minimax group fairness: Algorithms and experiments
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, and Aaron Roth · 2021
Closest in time.
Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
Closest in time.
Moment multicalibration for uncertainty estimation
Christopher Jung, Changhwa Lee, Mallesh M Pai, Aaron Roth, and Rakesh Vohra · 2021
Closest in time.
Mitigating bias in set selection with noisy protected attributes
Anay Mehrotra and L Elisa Celis · 2021
Closest in time.